Title: Simulation-based optimization in defense asset management and resource planning
A wide range of decision problems such as fleet size and mix, workforce and maintenance planning, facility location, and life-cycle analysis are covered in the military asset management and resource planning domain. These decision problems are usually unstructured, complex, and dynamic, as it exhibits several challenging features (e.g., path-dependence, dynamic complexity, and tightly coupled subsystems). System dynamics (SD), a system modeling and simulation methodology, is well-suited to deal with mentioned challenges due to its capability of capturing the feedback inter-dependencies between different parts of the system. In this research, a large-scale SD simulation model is built to cover and address the different aspects of asset management in the military. The stand-alone use of the model allows users to examine the performance of a strategy over time from both holistic and lifecycle viewpoints. Nevertheless, the developed SD model like any other simulation model neither suggests nor seeks the best/optimal strategy(ies). To alleviate the shortcomings of the SD model, we couple optimization algorithms (mostly metaheuristics including genetic algorithm and simulated annealing) with the developed SD, which is known as simulation-based optimization, to effectively search a very large set of feasible decision space to find optimal asset and resource management strategies. To test the applicability of the approach a real case study is used, which is motivated by the recent modernization efforts of the Australian Defence Force. The results obtained indicate that this approach leads to a considerable increase in operational readiness and identifies the causes of inferior performance.
Hasan H. Turan currently holds the research lead and a lecturer position at the Capability Systems Centre, University of New South Wales (UNSW Canberra). Before joining UNSW Canberra, he worked as a post-doc research fellow at Qatar University, Mechanical and Industrial Engineering Department from 2015 to 2017. He obtained his Ph.D. and master’s degrees both in Industrial and Systems Engineering from Istanbul Technical University and North Carolina State University, respectively.
Dr. Turan’s research interests revolve around the development and application of data-driven optimization algorithms and simulation models arising in different domains including service and maintenance logistics, defense applications (fleet management, workforce, and resource planning), energy capacity expansion, and telecommunications networks. He is currently focused on the integration of machine learning (e.g., reinforcement learning), artificial intelligence, and computational intelligence techniques (e.g., genetic algorithms) with simulation models (discrete event and system dynamics) to solve complex decision-making problems.
He has directed and participated in several projects in the mentioned areas supported by the Department of Defence, Qatar National Research Fund, The Scientific and Technological Research Council of Turkey and Balassi Institute.